Paper
SciForma: A Structure-Faithful Generation Framework for Scientific Diagrams
Peking University and Microsoft Research Asia propose the SciForma framework, which decomposes the quality of scientific diagrams into three structural axes: components, arrows, and text. They construct the SciFormaData-700K training set and the SciFormaBench-2K evaluation benchmark. Their core method, M-DPO, enforces correctness across all axes simultaneously after SFT, enabling SciForma-9B to surpass all open-source baselines and GPT-Image-1.5 on both benchmarks.
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